基于时频融合和注意力机制的深度学习行星齿轮箱故障诊断方法

针对行星齿轮箱振动信号频率成分复杂和时变性强的问题,提出了基于时频融合和注意力机制的深度学习行星齿轮箱故障诊断方法。首先,采用小波包分解将原始振动信号分解到频带和时间两个维度作为输入数据;然后,使用卷积神经网络融合数据的频带特征,使用双向门控循环单元融合时序特征;接着采用注意力结构对不同时间点的特征自适应地进行动态加权融合;最后通过分类器进行识别,实现行星齿轮箱的端对端故障诊断。实验表明,该方法对比现有的深度学习故障诊断模型具有更高准确率,能够对行星齿轮箱多种健康状态进行准确地诊断。

Fault diagnosis of planetary gearbox based on deep learning with time frequency fusion and attention mechanism

The vibration signal of planetary gearbox has the complexity of frequency component and time varying. To solve this problem, the fault diagnosis method based on deep learning with time frequency fusion and attention mechanism is proposed. Firstly, the wavelet packet decomposition is used to transform the original vibration signal into two dimensions of frequency band and time, which are adopted as input data. Then, the convolutional neural network is applied to fuse the frequency band characteristics of the data. The bidirectional gated recurrent unit is employed to fuse the timing features. The attention structure is adopted to weight and merge the features of different time point adaptively and dynamically. Finally, the classifier is used to identify the end to end fault diagnosis of the planetary gearbox. Experimental results show that this method has higher accuracy than the existing deep learning fault diagnosis model. It can accurately diagnose various health states of planetary gearbox.